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AI-assisted visit documentation with clinician sign-off

Structured intake and session data drafted into visit notes that clinicians review, edit and sign.

Illustrative scenario. Fictional behavioral health group. A fictional composite of the kind of work we do — not a real client engagement. Results marked [METRIC TO CONFIRM] are placeholders.

Client problem

Clinicians spent evenings finishing notes; templates didn't fit how sessions actually ran.

Existing environment

  • EHR with limited API
  • Paper and PDF intake forms
  • Measurement-based care questionnaires

Challenge

Make drafts genuinely useful without letting anything reach the record unreviewed — and prove quality over time.

Solution

A drafting step assembles a note from structured intake, questionnaires and prior context; the clinician edits and signs in a review screen; only signed notes are filed.

Architecture

  1. 01Intake + questionnaire capture
  2. 02LLM gateway with prompt versioning
  3. 03Evaluation suite on de-identified samples
  4. 04Review UI
  5. 05EHR write-back on signature

Technology

  • Next.js
  • Python
  • LLMs
  • PostgreSQL
  • AWS

AI integration

LLM drafting with strict templates and evaluation on every prompt change. Draft-vs-final differences are logged to measure and improve quality.

Workflow

  1. Session completed
  2. Structured data captured
  3. Draft generated
  4. Clinician edits
  5. Clinician signs
  6. Note filed to EHR

Result

  • [METRIC TO CONFIRM] reduction in after-hours documentation
  • Nothing filed without clinician signature
  • Measurable draft quality via edit distance

Technical highlights

  • Hard approval gate
  • Prompt + model versioning with eval suite
  • Audit of draft vs. final

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